Sentence Similarity
sentence-transformers
Safetensors
ministral3
text-embeddings
retrieval
rag
nvfp4
modelopt
vllm
8-bit precision
Instructions to use ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 248 Bytes
94a2ef2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"producer": {
"name": "modelopt",
"version": "0.45.0"
},
"quantization": {
"quant_algo": "NVFP4",
"kv_cache_quant_algo": null,
"group_size": 16,
"exclude_modules": [
"embed_tokens",
"lm_head"
]
}
}
|